package ds_industry_2025.industry.gy_10.T3

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

/*
    编写scala代码，使用Spark根据hudi_gy_dwd层的fact_produce_record表，基于全量历史数据计算各设备生产一个产品的平均耗时，
    produce_code_end_time值为1900-01-01 00:00:00的数据为脏数据，需要剔除，并以produce_record_id和ProduceMachineID为
    合主键进行去重（注：fact_produce_record表中，一条数据代表加工一个产品，produce_code_start_time字段为开始加工时间
    ，produce_code_end_time字段为完成加工时间），将设备每个产品的耗时与该设备平均耗时作比较，保留耗时高于平均值的产品数据，将
    得到的数据写入ClickHouse数据库shtd_industry的machine_produce_per_avgtime表中（表结构如下），然后在Linux的ClickHouse
    命令行中根据设备id降序排序查询前3条数据，将SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将
    执行结果截图粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下；
 */
object t4 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("t4")
      .config("hive.exec.dynamic.partition.mode","nonstrict")
      .config("spark.serializer","org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions","org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .config("spark.sql.legacy.parquet.datetimeRebaseModeInRead","LEGACY")
      .enableHiveSupport()
      .getOrCreate()


    val hdfs_path="hdfs://192.168.40.110:9000/user/hive/warehouse/hudi_gy_dwd.db/fact_produce_record"
    spark.read.format("hudi").load(hdfs_path)
      .where(col("producecodeendtime") !== lit("1900-01-01 00:00:00").cast("timestamp"))
      .dropDuplicates(Seq("producerecordid","producemachineid"))
      .createOrReplaceTempView("data")

    val result = spark.sql(
      """
        |select
        |*
        |from(
        |select distinct
        |produce_record_id,
        |produce_machine_id,
        |run_time as producetime,
        |round(avg(run_time) over(partition by produce_machine_id)) as produce_per_avgtime
        |from(
        |select
        |producerecordid as produce_record_id,
        |producemachineid as produce_machine_id,
        |unix_timestamp(producecodeendtime) - unix_timestamp(producecodestarttime) as run_time
        |from data
        |) as r1
        |) as r2
        |where producetime > produce_per_avgtime
        |""".stripMargin)

    result.write.format("jdbc")
      .option("url","jdbc:clickhouse://192.168.40.110:8123/shtd_industry")
      .option("user","default")
      .option("password","")
      .option("driver","com.clickhouse.jdbc.ClickHouseDriver")
      .option("dbtable","machine_produce_per_avgtime")
      .mode("append")
      .save()









    spark.close()

  }

}
